Novel Class Discovery in Chest X-rays via Paired Images and Text
Authors: Jiaying Zhou, Yang Liu, Qingchao Chen
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on eight subset splits of MIMIC-CXRJPG dataset show that our method improves the clustering performance of unlabeled classes by about 10% on average compared to state-of-the-art methods. |
| Researcher Affiliation | Academia | Jiaying Zhou1, 2, Yang Liu3, Qingchao Chen1, 2, 4 * 1National Institute of Health Data Science, Peking University, Beijing, China 2Institute of Medical Technology, Peking University Health Science Center, Beijing, China 3Wangxuan Institute of Computer Technology, Peking University, Beijing, China 4 National Key Laboratory of General Artificial Intelligence, Beijing, China |
| Pseudocode | No | The paper does not contain a pseudocode block or an explicitly labeled algorithm section. |
| Open Source Code | Yes | Code is available at: https://github.com/zzzzzzzzjy/MMNCD-main. |
| Open Datasets | Yes | MIMIC-CXR-JPG Dataset(Johnson et al. 2019b) This dataset contains 377,110 chest X-ray images from 65,379 patients. Each image is provided with 14 labels derived from two natural language processing tools applied to the corresponding free-text radiology reports. |
| Dataset Splits | No | The paper mentions 'Validation set accuracy' in Figure 1 (b) but does not provide explicit details on the size or split methodology for a validation dataset, such as percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used for experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper mentions the use of Res Net-50 and Bio Clinical BERT but does not specify any software dependencies with version numbers (e.g., Python, PyTorch, or other libraries). |
| Experiment Setup | Yes | We train our model in two stages... Then we conduct novel class discovery on our network with 200 epochs. All experiments are conducted with a fixed batch size of 128. |